Enhancing the quality of decoded quantized images

ABSTRACT

A system for image enhancement and, more particularly, a system for enhancing the quality of a quantized image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.10/762,443, filed Jan. 21, 2004 now U.S. Pat. No. 7,400,779, whichclaims the benefit of U.S. Provisional App. No. 60/535,917, filed Jan.8, 2004.

BACKGROUND OF THE INVENTION

This invention relates to a system for image enhancement and, moreparticularly, to a system for enhancing the quality of a quantizedimage.

As the state of the art of digital signal technology advances, relatedtechnologies such as digital image processing has experienced acorresponding growth and benefit. For example, the development andproliferation of facsimile communications allows images to be encodedinto digital signals, transmitted over conventional telephone line, anddecoded into a close representation of the original images. Image dataare also digitally encoded for ease of storage, modification, copying,etc. As is common experience with growing technologies, the field ofdigital image processing is also experiencing problems with applicationsin new areas.

Problems in the area of digital image processing relate generally toachieving a balance between acceptable image distortion and bit-depthrepresentations. In order to increase the efficiency and therefore theusefulness of digital image decoding schemes, the coding system mustprovide a coded set of image data that is more efficient to store,transmit, etc., than the original image data and must reproduce adecoded image with some minimum level of quality. However, theconversion of relatively high bit rate image data to lower bit rate datavirtually always entails a loss of image quality.

One straightforward method for digitizing an image is to create anartificial grid over the image and to assign a value to each grid spacerepresenting the color of the original image at that grid space. If thegrids are made small enough and the values represent a large enoughrange of color, then the image may be encoded and decoded with smallimage quality degradation. For example, display screen images are madeup of an array of pixels, i.e., picture elements. On a black and whitescreen, each pixel has a value of one or zero representing the on/offstate of the pixel. In a one-to-one bit-to-pixel coding scheme, eachpixel value is represented as a 1 or as a 0 and the entire screen imageis encoded. The result of the encoding is an array of binary values. Todecode the image, the array values are translated into a screen imagehaving pixels on or off in the same order in which they were originallyencoded.

If the image is comprised of more than two distinct colors, then morethan a 1-bit code must be used to represent the pixel values. Forexample, if four distinct colors are present in the image, a 2-bitbinary code can represent all of the values. If the image includes 256distinct colors, an 8-bit binary code is required to uniquely representeach of the color values. The memory requirements for such codingschemes increase as the number of distinct colors in the imageincreases. However, with high bit-depth representation schemes, thequality of the image that results will be good as long as the digitalimage transmission or recovery from storage is successful.

To reduce the size of the encoded digital image, the bit-depthrepresentation of the image may be reduced in some manner. For example,an image with a bit-depth of 6 bits per pixel requires significantlyless storage capacity and bandwidth for transmission than the same sizedimage with 16 bits per pixel.

Decoded images, constructed by a low bit-depth representation, generallysuffer from the following types of degradations: (a) quasi-constant orslowly varying regions suffer from contouring effects and amplifiedgranular noise, and (b) textured regions lose detail.

Contouring effects, which are the result of spatial variations, in adecoded image are generally fairly obvious to the naked eye. Thecontouring effects that appear in the slowly varying regions are alsocaused by the fact that not all of the variations in the intensity ofthe original image are available for the decoded image. For example, ifa region of the original image included an area having 4 intensitychanges therein, the decoded image might represent the area with only 2intensities. In contrast to the contouring effects, the effect of thegranular noise on the viewed image is often mitigated by the very natureof the textured regions. But it can be both amplified or suppressed dueto quantization, as well as altered in spectral appearance.

Kundu et al., U.S. Pat. No. 5,218,649, disclose an image processingtechnique that enhances images by reducing contouring effects. Theenhancement system identifies the edge and non-edge regions in thedecoded image. Different filters are applied to each of these regionsand then they are combined together. A low pass filter (LPF) is used onthe non-edge regions, and a high-pass enhancer is used on the edgeregions. Kundu et al. teaches that the contour artifacts are mostvisible in the non-edge areas, and the LPF will remove these edges.Unfortunately, problems arise in properly identifying the edge regions(is a steep slope an edge or a non-edge?). In addition, problems arisein setting thresholds in the segmentation process because if thecontours have a high enough amplitude, then they will be classified asedges in the segmentation, and thus not filtered out. Moreover, theimage segmentation requires a switch, or if statement, and two full-sizeimage buffers to store the edge map and smooth map, because the size ofthese regions varies from image to image, all of which is expensive andcomputationally intensive.

Chan, U.S. Pat. No. 5,651,078, discloses a system that reduces thecontouring in images reproduced from compressed video signals. Chanteaches that some contours result from discrete cosine transformation(DCT) compression, which are more commonly called blocking artifacts.Chan observes that this problem is most noticeable in the dark regionsof image and as a result adds noise to certain DCT coefficients when theblock's DC term is low (corresponding to a dark area). The resultingnoise masks the contour artifacts, however, the dark areas becomenoisier as a result. The dark areas thus have a noise similar tofilm-grain-like noise which may be preferable to the blocking artifacts.

What is desirable is a system for reducing contouring effects of animage. Because the system does not necessarily affect the encoding ortransmission processes, it can be readily integrated into establishedsystems.

BRIEF SUMMARY OF THE INVENTION

The foregoing and other objectives, features, and advantages of theinvention will be more readily understood upon consideration of thefollowing detailed description of the invention, taken in conjunctionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 illustrates a contrast sensitivity function.

FIG. 2 illustrates an image processing technique.

FIG. 3 illustrates a coring function.

FIG. 4 illustrates another coring function.

FIG. 5 illustrates another coring function.

FIG. 6 illustrates a 1-dimensional filtering of a sharp edge with noise.

FIGS. 7 A-D illustrate graphs of filtering.

FIGS. 8 A-D illustrate graphs of non-linear low pass filters.

FIG. 9 illustrates a block diagram of a non-linear sieve low passfilter.

FIG. 10 illustrates a block diagram of a non-linear sieve low passfilter.

FIG. 11 illustrates a block diagram of a non-linear low pass filter.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENT

The present inventor observed that the many of the objectionablecontouring effects in images appear in regions of the image that aregenerally free from a significant number of edges or otherwise hightexture. The present inventor similarly observed fewer objectionablecontouring effects in regions of the image that have a significantnumber of edges or otherwise high texture. After consideration of thisdifference in observing objectionable contouring effects, the presentinventor considered that the human visual system has lower sensitivityto these contour effects in the high frequency regions of the image andthe human visual system has higher sensitivity to these contour effectsin the low frequency regions of the image. FIG. 1 depicts the overallspatial response of the human visual system with its underlying visualchannels with the coarse quantization below the threshold. Further, themasking effects by the higher frequency content of the image, which islimited to the channels as shown in FIG. 1, further inhibits thevisibility of the steps in the waveforms in the high frequency regionsof the image.

In order to preserve the quality of the image, the system preferably(albeit not necessary) reduces the effects of the objectionable contourswithout having to add noise to the image in order to hide them. Thus thesystem is suitable for use with images that are otherwise free of imagecapture noise, such as computer graphics and line art with gradients.Moreover, it would be preferable (albeit not necessary) that thetechnique is implemented without decision steps or if statements toachieve computational efficiency. Also, the technique should require notmore than a single buffer of a size less than or equal to the image, andmore preferable a buffer less than 30, 20, or 10 percent of the size ofthe image.

Referring to FIG. 2, an input image 100 is provided with bit-depth P.The bit-depth P is frequently 8-bits for many images. As previouslydescribed, the image with a bit depth of P is normally quantized into2^(P) different values but may exhibit contouring effects when displayedon a display with a different bit depth, such as a 10 bit display. Inorder to reduce those aspects of the image that are likely to exhibitcontouring effects the present inventor came to the realization that theaspects of the image that will create contouring effects should beidentified in a suitable manner. In another case, the image may berepresented as P bits, but actually have quantization artifacts due toless than P bits. An example is a DVD image which is represented at 8bits/color but only has 6 bits/color of real information because of thequantization of P or B-frames, or in the YCbCr to RGB conversion. Inmany cases, such as DVD applications, the bit-depth limitation comesfrom the inaccurate color matrix calculation (e.g., insufficientbit-depth in the registers).

To reduce the false step edges of contouring the image is preferablylow-pass filtered 102. The low-pass filter applied to the image alsoacts to increase the bit depth, since the low pass filter is primarilyan averaging operation (averaging across a neighborhood of pixels), andthe averaging results in a higher precision. Other techniques maylikewise be applied to effectively modify the bit depth of the image. Insome cases, the system may also modify the bit depth by switching thenumber of bits used for processing the image, such as 8 bits to 16 bits.One way of characterizing an increase in the bit depth of an image is tomodify in any manner an image to 2^(N) different levels, where N≠P.Alternatively, the image may already have a bit-depth needed/desired forthe output (P=N), but the image itself may have a limited bit depth of P(P<N) from a previous operation. In this case, the value of P should beknown or otherwise determined. Alternatively, the image may have abit-depth of N, but the image itself may have a limited bit-depth of P(P<N).

The result of the low pass filtering of the image is to modify the imageto achieve a bit depth of N. In most cases the bit depth N is thedesired bit depth in the final image, such as an image having bit depthN to be displayed on a N bit display. The low pass filter should besufficiently wide (in the spatial domain) to reduce most false steps(contouring) due to the bit-depth limit P. It is noted that the low passfilter likewise reduces other desirable image information within theimage, so merely low-pass filtering the image is insufficient. Note thatthe low pass filter also reduces much useful image information, such asby severely blurring the image, so this step is insufficient to properlyrectify the undesirable contouring.

The low pass filter 102 may be implemented as Cartesian-separable1-dimensional filters. The use of a pair of 1-dimensional filters ismore computationally efficient than a 2-dimensional filter. The filterpreferably has a rectangular shape. The size of the low pass filterkernel may be based upon the viewing distance and the pixels per inch ofthe display. For a 90 pixel per inch display viewed at 1000 pixelviewing distance, the kernel size should be more than 11 pixels toremove the visibility of a contour edge, but the present inventordetermined that the kernel size should actually be more than 31 pixelsto remove the low frequency modulation component of the false contour.Accordingly, the kernel size should be based upon the low frequencymodulation component of the false contour, as opposed to merely thevisibility of the contour edge.

The resulting image from the low pass filter is primarily the lowfrequency components of the image. It is likewise to be understood thatany suitable filter may be used, such as one that generally attenuatesthe high frequency components with respect to the low frequencycomponents of the image.

The system may subtract 105 the low pass filtered image 102 from theoriginal bit-depth limited image 100 having false contours. This inessence reduces the low-frequency content of the input image, whileprimarily leaving the high frequency content. However, the subtraction105 also results in another attribute, namely, the high frequencyportion of the remaining image contains the high frequency portion ofthe contour artifacts. The result of this operation may be referred toas the HP component 107. It is also noted that the result of the lowpass filter 102 is that the low frequency portion of the remaining imagefrom low pass filter 102 contains the low frequency portion of thecontour artifacts. Accordingly, the contouring artifacts are separatedin some manner between the low frequency and high frequency componentsof the image.

The subtraction process 105 leads to a bit-depth increase of 1 due tothe sign. For example a subtraction of two images with 0 to +255 resultsin an image with a range from −255 to +255. Hence the high passcomponent 107 has N+1 bit depth (this is based upon a source image beingpadded to N bits before the subtraction operation). Padding may includeinserting 0's and/or 1's to the least significant bits that are missingfrom the lower bit depth representations relative to the higher.

It is noted that the output of the system may not need N+1 bits, but itshould be able to carry a sign, and thus have a zero mean level. If onlyN bits are used and one bit is dedicated to the sign, then only N−1 bitsare available for addition to the low pass filter image (in the lastaddition step). In that case some edge sharpness and detail may be lost.

As previously described, the result of subtracting the low pass filteredimage from the original image results in an image that maintains highfrequency false contour information. It has been determined that thehigh frequency false contour information that should be reduced arethose having a low amplitude. Accordingly, the low amplitude informationshould be reduced with respect to the high amplitude information. Toreduce the low amplitude high frequency false contour information acoring function 110 may be applied. Other techniques may likewise beused, if desired. The coring function 110 may include a hard-thresholdcoring function, such as for example, if abs(HP)<b then HP=0, elseHP=HP. This effectively reduces the contours, especially if the low passfilter is sufficiently large.

Unfortunately, simply applying a hard-threshold coring function, whileacceptable, resulted in unexpected additional artifacts that appear likeislands of color, and as ringing of step edges. After consideration ofthese unexpected artifacts, it was determined that a transitioned coringfunction will both reduce the low amplitude high frequency false contourinformation, and reduce the additional color islands and ringing ofsteps edges. A modified transitional coring function, may be forexample, as:CVout=sign(CVin)*A[0.5−0.5 cos(α|CVin|)] for |CVin|<MCVout=CVin for |CVin|≧M  (1)

CVin is the input code value of the HP image to the coring function,while CVout is the output code value of the coring function. M is themerge point that is where the coring behavior ends (or substantiallyends) and the coring function returns to the identify function (or othersuitable function). A and α are parameters selected to ensure twoconditions at the merge point, M, namely:

-   -   (A) amplitude=M    -   (B) slope=1

The first criterion ensures that the coring function has nodiscontinuity in actual value, and the second ensures that the 1^(st)derivative is continuous. The first criterion keeps the tone scalemonotonic in the HP band, and the second avoids mach band typeartifacts. Thus this coring function could even be applied to the lowpass band without such artifacts. It is noted that the coring functionpreferably has a slope that equals 1 that intersects with the origin ofthe plot as well as no second order discontinuities.

The criteria may be restated as follows:M=A[0.5−0.5 cos(αM)] for the amplitude  (2)1=d/dCVin(A[0.5−0.5 cos(αCVin)])|CVin=M  (3)

An example of the coring functions for the value of M=8 with A=11.3 andα=0.25, is shown in FIG. 3 to illustrate the actual mapping due to theeffects of the quantization to N bits. Curve 50 has a slope equal to 1.The curve 52 is equation (1) with the parameters to achieve a mergepoint at 8. The curve 54 are the actual code values if the HP image isquantized to N bits. The curve 56 is the slope of the scaled cosinefunction in equation (1), before the merge point. It is noted that onlythe positive half of CVin is illustrated.

Another example of the coring function for the value of M=4 with A=5.6and α=0.5 is illustrated in FIG. 4. Another example of the coringfunction for the value of M=16 with A=19.75 and α=0.14 is illustrated inFIG. 5.

Referring again to FIG. 2, the next step is to add the cored image 110and the filtered low pass components 102 together at 112. This operationrestores the low frequency information that was reduced back to theimage. The result of this addition operation is N+1 bits. It is N+1since the range may be larger than the input image (for example if thelow pass component for N bits may be 0 to +255, the high pass componentmay have a range of N+1 (−255 to +255) and the result is −255 to +512).Anything out of the range of N bits is clipped (e.g., out of range 0 to+255 for N=8). It turns out that there are a limited number of pixelsthat fall out of that range, and when they do, they are usually isolatededge pixels. The clipping that occurs as a result is not readily visiblein the final image 114.

The number of gray levels is given by N or P, which are bit-depths, sothe numbers of levels is 2^(N) or 2^(P) However, another embodiment isto not use bit-depth to determine the number of gray levels, but thenumbers of gray levels directly.

In a particular implementation, each of the steps shown in FIG. 2 may beapplied to the entire image in a sequential manner, which may result inthe need for large buffers. However, a more memory efficient techniqueinvolves using as a sliding window, where the computations within thewindow are used to compute the pixel at the center of the window (or insome other position in the window). This reduces the memoryrequirements.

In a typical implementation the bit-depth of the input and output imagesare known in advance. This is typically the case in many applications,such as display an eight bit image on a 10-bit display.

While the implementation illustrated in FIG. 2 effective reduces thecontour artifacts, it was determined that it also results in ghostartifacts in some areas of some images. Upon further analysis thepresent inventors came to the realization that the ghost artifacts occurin regions with sufficiently sharp edge features. While such a result issatisfactory for most cases because the viewer is unaware of exactly howthe image should really appear, it turns out to be particularlydetrimental in the facial region of an image, where there is a higherexpectation of the appropriate texture by the typical viewer.

To reduce the occurrence of ghosting artifacts, the present inventorscame to the realization that a linear low pass filter tends to smearsharp edges because a sharp edge has high energy on the high-passfrequency band and this energy does not efficiently pass through the lowpass filter. When a pixel with value I is close to a sharp edge, theoutput of the low pass filter, the low pass component I_(L), is quitedifferent from I. In other words, the high pass component I_(H)=I−I_(L)is substantial. Because the selected coring functions passes its inputsignal without modification to its output if an input-signal I_(H) islarger than a threshold, the substantial I_(H) is unchanged after coringand consequently I is unchanged. As a result, the decontouring techniqueusing a linear low pass filter maintains areas close to sharp edgesunchanged. With noise in other areas is reduced, the noise in the areasclose to sharp edges become prominent. In other words, the noise inregions not sufficiently proximate the sufficiently sharp edges isreduced to a greater extent than the noise in regions sufficientlyproximate the sharp edges. This phenomena is further illustrated byFIGS. 6 and 7.

FIG. 6 illustrates a 1-dimensional signal with a sharp edge and somenoise. FIG. 7( a) illustrates the results of linear low pass filterbeing applied to this signal. The sharp edge in FIG. 6 becomes a slopein FIG. 7( a). FIG. 7( b) illustrates the results of high passfiltering. In the region around the sharp edge, there are twosubstantial spikes. The widths of the spikes are typically equal to thelength of the filter, which in this illustrate is 31. FIG. 7( c)illustrates the high pass filter signals after coring. The two spikesremain unchanged while other fluctuations are significantly smooth bythe coring function. FIG. 7( d) illustrates the final result by addingthe low pass filter and the cored high pass filter. As it may beobserved, there remain ghost effects around the sufficiently sharpedges. Accordingly, it is desirable to reduce or otherwise eliminate theenergy remaining near an edge to substantially the same level as theenergy remaining in regions not near an edge.

A modified low pass filter may be used to preserve sharp edges. Forexample, a Sieve filter may be designed to be a low pass filter whilepreserving sharp edges. The sieve filter utilizes a 1-dimensional or2-dimensional rectangular or other suitably shaped window, where thecurrent pixel I(x,y) is at the center of the window. The filter maycompare all the pixels I(i,j) in the window with the central pixelI(x,y). The filter may average those pixels whose value differences withthe central pixel I(x,y) is within a threshold T. Because this filterdrops pixels that are not within the threshold, one may refer to thistype of filter as a sieve filter. Because a sieve filter does notsatisfy the conditions of linear filter, it is referred to as anonlinear filter. Mathematically, the output of the sieve filter,I_(LP)(x,y), is calculated by

${I_{LP}\left( {x,y} \right)} = \frac{\sum\limits_{{{{{({i,j})} \in E}\;\&}\;{{{I{({i,j})}} - {I{({x,y})}}}}} < T}{I\left( {i,j} \right)}}{N\left( {x,y} \right)}$where E is the window; N(x,y) is the count of the pixels in E thatsatisfy the condition of |I(i,j)−I(x,y)|<T. In some cases, a sievefilter may be incorporated with any de-contouring technique or otherwisean artifact reduction technique since the filter tends to estimate thedesirable signal from its noisy observation.

FIG. 8 show that the same signal in FIG. 6 processed by the sievefilter. FIG. 5( a) shows the results of non-linear sieve low pass filterapplied to this signal. When the threshold T is set to be 16, the sharpedge is preserved, while FIG. 7( a) is a slope. FIG. 8( b) shows theresults of high pass filter. Contrast to FIG. 7( b), in the area aroundthe sharp edge, there are no spikes. FIG. 8( c) shows the high passfilter signals after coring. All substantial fluctuations are removed bythe coring function. FIG. 8( d) shows the final result by adding lowpass filter and the cored high pass filter. There are no ghost effectsaround the sharp edge.

A block-diagram of an exemplary sieve filter is shown in FIG. 9.

One technique to determine the threshold is by presetting or learningbased on the local information. Learning may use histogram of thewindow.

The sieve filter mathematically defined by Equation (1) and illustratedby the diagram of FIG. 9 is problematic to implement by the hardware.Therefore, an alternative version of the sieve filter may defined by thefollowing equation

${I_{LP}\left( {x,y} \right)} = \frac{\sum\limits_{{{{{({i,j})} \in E}\;\&}\;{{{I{({i,j})}} - {I{({x,y})}}}}} < T}{{I\left( {i,j} \right)} \times \left( {N_{total}{N\left( {x,y} \right)}} \right) \times {I\left( {x,y} \right)}}}{N_{total}}$

When a pixel's difference with the central pixel is bigger than thethreshold T, instead of dropping the pixel from summation in Equation(1), Equation (2) replaces the pixel's value with the central pixel'svalue in summation. Equation (2) has the advantage over Equation (1)that the denominator in Equation (2) is fixed, which is preferable byhardware implementation. The corresponding diagram is shown in FIG. 10.

The implementation hardware block-diagram using a sieve filter(horizontal 31 taps and vertical 5 taps) is shown in FIG. 11. Thediagram utilizes Equation (2). The sieve filter is generally notCartesian-separable. To simplify the requirement for calculation, onemay apply the sieve filter horizontally. Specifically, as shown in FIG.10, the technique may average the vertical pixels first, and then applyEquation (2) to the 31 averaged data horizontally.

As it may be observed the low pass filter may be any non-linear low passfilter, as desired. In addition, the low pass filter may be anyspatially-varying low pass filter, as desired. Also, the low pass filtermay be any spatially-varying non-linear low pass filter.

It is noted that if the system has access to the image and it isdesirable to reduce the bit-depth in subsequent image processing, suchas display on a lower bit depth liquid crystal display, whilemaintaining a high image quality for the reduced bit depth, then aparticular class of image processing techniques may be used, such as forexample, dithering techniques. Several different types of ditheringtechniques have been developed, such as for example, amplitudedithering, spatial dithering, and phase dithering. For example, aproperly designed amplitude dither technique may preserve the imageentropy at low frequencies, while allowing for entropy losses at highfrequencies.

However, if the image that is acquired already has a lower bit-depththan desired, and a dithering technique was not previously applied tothe image, then the dithering techniques do not assist in removing theundesirable distortion. In other words, dithering techniques aretraditionally used for pre-processing of images. The reason for thispre-processing limitation is because the entropy loss has alreadyoccurred at the low frequencies due to quantization, which acts toreduce the amplitude resolution uniformly across all frequencies.

While the described implementations effectively reduce the contourartifacts, it was determined that it also removes a significant amountof low amplitude detail. While such a result is satisfactory for mostcases because the viewer is unaware of exactly how the image shouldreally appear, it turns out to be particularly detrimental in the facialregion of an image, where there is a higher expectation of theappropriate texture by the typical viewer.

To reduce the loss of low amplitude texture detailed, the presentinventors came to the realization that one may separate the case wherethe low amplitude information is due to the image texture (which isgenerally isotropic) versus the case when the low amplitude informationis due to a false contour (which is generally non-isotropic, a.k.a.,structured error). Generally isotropic information (“iso” meaning one orthe same, and “tropic” meaning space or direction) has generally thesame texture in all directions. For example, clean sand on the beach maybe considered generally isotropic. For example, generally non-isotropicinformation has different textures in different directions. In manycases, the non-isotopic information has the characteristic of an edge.Accordingly, generally isotropic information has more uniform texturerelative to generally non-isotropic information. This additionalfiltering may be included with the coring.

While a plurality of different filters may be used, it is preferable touse a single filter, which is more computationally efficient in manycases. The accumulation of local special activity may be determined in asuitable manner, such as for example, a single sigma type filter or asingle sum of absolute differences (“SAD”) type filter.

The preferred system uses a version where the lowpass filter isimplemented in two Cartesian separable steps (i.e., a cascade of H and Vsteps.), and the filter width spatially is =31 pixels, with a uniform(rectangular) impulse response. For many images a filter width of 17 or25 is acceptable. Typically a width less than 17 does not workexceptionally well for image of size greater than 512×512.

The system may accumulate local activity, if desired, over a localregion around a pixel of the image to be cored (examples includestandard deviation, a, over a local 9×9 window, or SAD, over a similarlocal window). One may select different coring functions based on alocal activity index (or continuously adjust coring function based on alocal activity index). Local activity measure could be taken from themean of the local window, but is more preferably around the zeroactivity point of the HP image (i.e., 0, unless a pedestal offset isused.). The coring function has merge point where coring reverts to anidentity function. At this merge point, the slope of the coring equalsthe slope of the identity function (typically=1).

The processing of the image may result in an image having a bit depthgreater than the desired output bit-depth “N” for the image.Accordingly, the bit depth of the input image may have been the same as,or less than, the desired bit-depth of the output image which during theimage processing technique for reducing the contouring effects isincreased to a bit-depth greater than the desired bit-depth of theoutput image. The present inventors came to the realization that with animage having a greater bit depth than necessary for the output image, asa result of previous image processing for de-contouring effects (e.g.,low pass filtering), there is the potential that a dithering technique(which may be included together with the coring) (e.g., bit depthextension) may be applied to the image when reducing the bit depth tothe desired output bit depth. As previously discussed, ditheringtechniques are typically not considered applicable for overcomingbit-depth limitations for image processing when the image provided has abit depth less than the bit depth of the output image. The image may bemodified with a dithering technique, such as for example, amplitudedithering, spatial dithering, and phase dithering. The ditheringtechnique may allow the bit depth to be reduced without noticeablevisible loss from R+1 to N. Preferably, an amplitude based ditherpattern is applied that is characterized by the frequency spectrum thatis approximately matched to the inverse of the human visual spatialfrequency response.

Another technique that may be applied is to increase the bit depth ofthe high pass component 107 to a greater bit depth, such as “R”. In thismanner, a more accurate coring function may be applied in the R-bitspace. In some implementations, this may be accomplished by modifyingthe bit depth of the input image to R bits before the subtraction thatform the high pass component 107.

The dithering technique may be applied to the low pass filtered image,if desired. This reduces the effects of contouring effects as a resultof quantization while similarly increasing the quality of the outputimage.

The dithering technique may be applied to the input image by firstexpanding the bit depth of the image. While spectrally shaped “noise”may be used, in addition “white” noise may likewise be used, if desired.The low pass filter will effectively modify the “white” noise to a “highpass” noise, which has the general desired spectral characteristics.

1. A method for modifying an image comprising: (a) receiving an imagehaving a first bit depth; (b) modifying said image using a Sieve filterperforming a summation operation that replaces the value of a targetpixel with the value of central pixel in said summation operation whensaid target pixel differs from said central pixel by a value larger thana threshold to create a modified image resulting in a second bit depthdifferent than said first bit depth in such a manner that the higherfrequency content with respect to the lower frequency content of saidimage is attenuated, and attenuating the lower amplitude content of saidhigher frequency content with respect to the higher amplitude content ofsaid higher frequency content; (c) modifying said modified image basedupon said modified image and said lower frequency content of said image.2. The method of claim 1 wherein said attenuation of said higherfrequency content with respect to said lower frequency content is basedupon a non-linear process.
 3. The method of claim 1 wherein saidattenuation of said higher frequency content with respect to said lowerfrequency content is a spatially varying process.
 4. The method of claim1 wherein said attenuation of said higher frequency content with respectto said lower frequency content is spatially varying non-linear process.5. The method of claim 1 wherein said attenuating the lower amplitudecontent of said higher frequency content with respect to the higheramplitude content of said higher frequency content includes a coringfunction.
 6. The method of claim 5 wherein said coring function includesa hard-threshold.
 7. The method of claim 5 wherein said coring functionincludes a transitional coring function.
 8. The method of claim 5wherein said coring function includes no discontinuity in actual value.9. The method of claim 1 wherein said received image of step (a) isrepresented by X bit depth.
 10. The method of claim 9 wherein saidmodified image of step (b) is represented by Y bit depth.
 11. The methodof claim 10 wherein X>Y.
 12. The method of claim 1 wherein saidmodifying of step (b) is a low pass filter.
 13. The method of claim 1wherein said modifying of step (b) wherein said lower frequency contentis amplified.
 14. The method of claim 1 wherein said modifying of step(b) changes the physical bit depth representation of the image.
 15. Themethod of claim 1 wherein said modifying of step (b) does not change thephysical bit depth representation of the image.
 16. The method of claim1 being performed in a manner free from including conditionalstatements.
 17. The method of claim 1 being performed in a manner usinga buffer smaller than 100 percent of said received image.
 18. The methodof claim 1 being performed in a manner using a buffer smaller than 30percent of said received image.
 19. The method of claim 1 beingperformed in a manner that is free from adding additional noise to saidimage.
 20. The method of claim 1 being performed in a manner based uponthe human visual system.
 21. The method of claim 1 wherein saidmodifying includes reducing noise in regions proximate edgessubstantially to the same extent as noise in regions not proximate saidedges.
 22. A method for modifying an image comprising: (a) receiving animage having a first bit depth wherein modifying said image to a secondbit depth would result in artifacts, wherein said first bit depth isless than said second bit depth; (b) modifying said image to another bitdepth different than said first bit depth in such a manner that thelower amplitude higher frequency content with respect to the higheramplitude lower frequency content of said image is attenuated to reducesaid artifacts that would have otherwise occurred; (c) wherein saidmodifying includes using a Sieve filter.
 23. A method for modifying animage comprising: (a) receiving an image having a first bit depthwherein modifying said image to a second bit depth would result inartifacts, wherein said first bit depth is less than said second bitdepth; (b) modifying said image to another bit depth different than saidfirst bit depth in such a manner that the lower amplitude higherfrequency content with respect to the higher amplitude lower frequencycontent of said image is attenuated to reduce said artifacts that wouldhave otherwise occurred; (c) wherein said modifying includes using aSieve filter.